Coaching the Exploration and Exploitation in Active Learning for Interactive Video Retrieval

2013 ◽  
Vol 22 (3) ◽  
pp. 955-968 ◽  
Author(s):  
Xiao-Yong Wei ◽  
Zhen-Qun Yang
Keyword(s):  
Active Learning ◽  
Interactive Video ◽  
Video Retrieval ◽  
Exploration And Exploitation ◽  
Interactive Video Retrieval

A Learned Lexicon-Driven Paradigm for Interactive Video Retrieval

2007 ◽  
Vol 9 (2) ◽  
pp. 280-292 ◽  
Author(s):  
C.G.M. Snoek ◽  
M. Worring ◽  
D.C. Koelma ◽  
A.W.M. Smeulders
Keyword(s):  
Interactive Video ◽  
Video Retrieval ◽  
Interactive Video Retrieval

Towards hierarchical context: unfolding visual community potential for interactive video retrieval

2010 ◽  
Vol 55 (1) ◽  
pp. 151-178 ◽  
Author(s):  
Lin Pang ◽  
Juan Cao ◽  
Lei Bao ◽  
Yongdong Zhang ◽  
Shouxun Lin
Keyword(s):  
Interactive Video ◽  
Video Retrieval ◽  
Interactive Video Retrieval

Interactive Video Retrieval in the Age of Deep Learning – Detailed Evaluation of VBS 2019

2021 ◽  
Vol 23 ◽  
pp. 243-256 ◽  
Author(s):  
Luca Rossetto ◽  
Ralph Gasser ◽  
Jakub Lokoc ◽  
Werner Bailer ◽  
Klaus Schoeffmann ◽  
...  
Keyword(s):  
Deep Learning ◽  
Interactive Video ◽  
Video Retrieval ◽  
Detailed Evaluation ◽  
Interactive Video Retrieval

scholarly journals Competitive Interactive Video Retrieval in Virtual Reality with vitrivr-VR

2021 ◽  
pp. 441-447 ◽  
Author(s):  
Florian Spiess ◽  
Ralph Gasser ◽  
Silvan Heller ◽  
Luca Rossetto ◽  
Loris Sauter ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Interactive Video ◽  
Video Retrieval ◽  
Interactive Video Retrieval

scholarly journals Utilizing Implicit User Feedback to Improve Interactive Video Retrieval

2011 ◽  
Vol 2011 ◽  
pp. 1-18 ◽  
Author(s):  
Stefanos Vrochidis ◽  
Ioannis Kompatsiaris ◽  
Ioannis Patras
Keyword(s):  
Support Vector Machine Classifier ◽  
Interactive Video ◽  
Video Retrieval ◽  
User Interaction ◽  
User Feedback ◽  
Visual Features ◽  
Support Vector ◽  
Feedback Information ◽  
Feedback Analysis ◽  
Interactive Video Retrieval

This paper describes an approach to exploit the implicit user feedback gathered during interactive video retrieval tasks. We propose a framework, where the video is first indexed according to temporal, textual, and visual features and then implicit user feedback analysis is realized using a graph-based methodology. The generated graph encodes the semantic relations between video segments based on past user interaction and is subsequently used to generate recommendations. Moreover, we combine the visual features and implicit feedback information by training a support vector machine classifier with examples generated from the aforementioned graph in order to optimize the query by visual example search. The proposed framework is evaluated by conducting real-user experiments. The results demonstrate that significant improvement in terms of precision and recall is reported after the exploitation of implicit user feedback, while an improved ranking is presented in most of the evaluated queries by visual example.

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